Hi Anderson,
Thanks! That clarifies alot!
I have now applied the first design in Glm:
Group = groups (25 ones, 25 twos)
EV1 = group1 (25 ones, 25 zeros)
EV2 = group2 (25 zeros, 25 ones)
EV3 = nuisance variable (50 age values, demeaned)
However, probably due to the fact that I do not split the age variable in
two groups, I get the following warning:
'Problem with processing the model: Warning - design matrix uses different
groups (for different variances), but these do not contain "separable" EVs
for the different groups (...)'
I expect to get this warning also in the design with the behavioral
measurements, since I do not split age there either, but do define two
groups in the 'group' EV.
Can I ignore this message, since the age variable is nuisance? Or should I
use the groups file to define exchangeability blocks? (Until now I have
never done that..)
Best,
Z.
On Mon, Feb 23, 2015 at 9:01 AM, Anderson M. Winkler <[log in to unmask]
> wrote:
> Hi Zsuzsi,
>
> I take that you meant to send this to the list, so I'm forwarding. Please,
> see below:
>
>
> On 22 February 2015 at 11:40, Z. Sjoerds <[log in to unmask]> wrote:
>
>> Hi Anderson!
>>
>> Thanks so much for your help! It seems a struggle to me, to fully
>> comprehend why you make certain choices for a design matrix. But I do want
>> to fully comprehend, si I am sure I do the right thing in the future.
>>
>> An issue that is left for me, is what you say in the beginning of your
>> last email: all EVs that are set to 0 are nuisance variables. This is what
>> I know also from the SPM design matrices. Statistically this means that the
>> variance explained by these variables is left out ('regressed out') in your
>> test. But when I compare the two groups on FA value (independent of
>> behavioral outcome; in principle a whole different question), I don't want
>> any variance explained by the behavioral measurements to be taken out. I
>> might miss some important group differences due to the fact that one of the
>> behavioral measurements differs significantly between groups. At this level
>> of hypothesizing, I don't want anything to do with the behavioral
>> measurements (yet). Only in the later step, when I regress FA value with
>> behavior, I could isolate a possible group*behav effect.
>>
>
> With 50 subjects, these 4 EVs will hardly become an issue, so I don't
> think there's a need for multiple models. But if you note that the EVs with
> the behavioural variable are not significant, and if it isn't anyway a
> sensible hypothesis or evidence that they'd somehow be associated with FA,
> you can remove them from the model and run just a 2 group comparison
> (presumably with age, as you mentioned).
>
>
>> So therefore I thought that I need two design matrices: one for the
>> simple FA group comparison:
>>
>> Group = group (25 ones, 25 twos)
>> EV1 = group1 (25 ones, 25 zeros)
>> EV2 = group2 (25 zeros, 25 ones)
>> EV3 = nuisance variable (age, demeaned)
>>
>> Then the contrasts of interest would be:
>> C1 = F-contrast: 1 -1 (main effect of group)
>> C2 = T-contrast: 1 -1 (post-hoc group1 > group2)
>> C3 = T-contrast: -1 1 (post-hoc group2 > group1)
>>
>> This way I actually regress out the only nuisance variable of interest
>> (and no additional variables that are not nuisance to me)
>> Then I will run the Two-Sample t-test:
>>
>> randomise -i AllFAmerged4D -o TwoSampT -d design.mat -t design.com -mask
>> FAthresholdMask -x --T2
>>
>
> There's no need for the F-test, although if you use randomise as shown, it
> won't make any difference because you didn't use the "-f design.fts". If
> the reason for the F-test is to account for multiple testing, you can use
> Bonferroni (0.05/2), It won't be conservative in this case.
>
> Also, variables that aren't in the contrast *are* nuisance, regardless of
> them being in fact interesting in some other contrasts (or in any other
> way) or not. And in general, if a variable may affect the response variable
> (or is known to affect), it can be kept, even if not significant, as it
> absorbs some of the variance and makes the test for the other variables
> more powerful.
>
>
>>
>> (Importantly: The 4D file is ordered according to participant number, not
>> according to group. But ofcourse in my design I kept the same order, so
>> e.g. for the first columns group: 1 2 1 1 2 2 1 2 2 1 1 1 2 2 1 2 2 1 1 2
>> etc.. and the same order for EV1 and EV2; I assume that is fine?)
>>
>
> The order of the rows don't matter as long as they match the 4D file.
>
>
>>
>> Second, I will make a design matrix for the second, behavioral-related
>> question (and then defining both behav measurements in one design):
>>
>> Group = group (25 ones, 25 twos)
>> EV1 = group 1 * behav 1
>> EV2 = group 2 * behav 1
>> EV3 = group 1 * behav 2
>> EV4 = group 2 * behav 2
>> EV5 = nuisance variable (age, demeaned)
>> ***So, do I need an intercept here???*** or do I need to define the two
>> groups by two extra EVs here anyways?
>>
>
> Must include the groups.
>
>
>>
>> And then the contrasts of interest:
>>
>> C1 = T-contrast: 1 1 0 0 0 (main positive effect of behav1, independent
>> of group)
>> C2 = T-contrast: -1 -1 0 0 0 (main negative effect of behav1, independent
>> of group)
>> C3 = T-contrast: 0 0 1 1 0 (main positive effect of behav2, independent
>> of group)
>> C4 = T-contrast: 0 0 -1 -1 0 (main negative effect of behav2, independent
>> of group)
>> C5 = F-contrast: 1 -1 0 0 0 (group interaction test on behav1)
>> C6 = T-contrast: 1 -1 0 0 0 (post-hoc t-test group1*behav1 > group2
>> *behav1)
>> C7 = T-contrast: -1 1 0 0 0 (post-hoc t-test group1*behav1 < group2
>> *behav1)
>> C8 = F-contrast: 1 -1 0 0 0 (group interaction test on behav2)
>> C9 = T-contrast: 1 -1 0 0 0 (post-hoc t-test group1*behav2 > group2
>> *behav2)
>> C10 = T-contrast: -1 1 0 0 0 (post-hoc t-test group1*behav2 < group2
>> *behav2)
>>
>> Is this correct??
>>
>
> I think this was answered in an earlier email, just check that...
> No need for these F-tests, just use Bonferroni.
>
>
>
>> Sorry if I keep on repeating myself, but I am afraid I have been doing
>> things wrong until now, and want to make 1000% sure that I do it correctly
>> now, and also understand it independently, so I don't have to bother you in
>> the future anymore ;-)
>> By the way, the wiki for Glm / Randomise is offline from the fmrib site.
>> Is there a reason for that? I tried to find some answers there, but that
>> didn't work out..
>>
>
> It should be back now.
>
> All the best,
>
> Anderson
>
>
>
>
>>
>> Thanks again; hope you can help comprehend the last issues!
>>
>> Best,
>> Zsuzsi
>>
>>
>>
>> On Sat, Feb 21, 2015 at 6:21 PM, Anderson M. Winkler <
>> [log in to unmask]> wrote:
>>
>>> Hi Zsuzsi,
>>>
>>> Please see below:
>>>
>>> On 21 February 2015 at 14:56, Z. Sjoerds <[log in to unmask]> wrote:
>>>
>>>> Hi Anderson,
>>>>
>>>> Thanks for your quick reply!
>>>> I am slightly surprised that I can put all EVs and contrasts in one
>>>> design; always understood to make different designs for different
>>>> questions, especially when it involves two different statistical tests:
>>>> 2-sample t-test for FA comparison between groups, versus multiple
>>>> regression for group comparisons on slope with a continuous variable (e.g.
>>>> behavioral measure).
>>>>
>>>
>>> It's all the same -- all particular cases of the same model.
>>>
>>>
>>>
>>>> So, how does that work then, for the fact that age is nuisance here,
>>>> but the behav EVs are not, especially in the case of 'simple' t-test group
>>>> comparison of FA, irrespective of behavioral measurement?
>>>>
>>>
>>> Each contrast is tested separately, and for each contrast, the EVs
>>> marked as 0 are nuisance. The others (non-zero) are effects of interest.
>>>
>>>
>>>
>>>> Isn't there then also somehow a correction for the behav scores, as is
>>>> for age, and since the groups differ in one of the behav scores, don't I
>>>> then remove an important part of the explained variance between the groups
>>>> (in the simple FA 2-sample t-test)?
>>>>
>>>
>>> Given behav1 and behav2 are correlated, it's impossible to disambiguate
>>> them, and as coded, each test with check the unique contribution of the
>>> respective scores. It may not be what you want, hence the suggestion to
>>> collapse both scores with PCA. I think you mentioned that the correlation
>>> is high, and in this case, another possibility is simply ignore one of them
>>> (that is, remove either behav 1 or behav 2 it from the design altogether).
>>>
>>>
>>>
>>>> And I assume with C5 and C6 you don't mean an interaction? Because my
>>>> first, main question regards the simple FA comparison between groups. The
>>>> whole-sample (two groups pooled) regression with the behavs and group*behav
>>>> interaction are part of my second question.
>>>>
>>>
>>> Yes, sorry, the C5 and C6 are not for interactions, just group
>>> differences:
>>>
>>> C5: [1 -1 0 0 0 0 0]: (group 1 > group 2)
>>> C6: [-1 1 0 0 0 0 0]: (group 1 < group 2)
>>>
>>>
>>>
>>>> Shouldn't there also be contrasts that look at whole-sample regression
>>>> with behav? e.g. 0 0 1 1 0 0 (positive association behav 1), 0 0 -1 -1 0 0
>>>> (negative association with behav 1), 0 0 0 0 1 1 (positive association with
>>>> behav 2), 0 0 0 0 -1 -1 (negative association with behav 2)?
>>>>
>>>
>>> If you want, yes, but if the interaction is significant, these contrasts
>>> won't be useful. And if the interaction isn't significant, a single EV for
>>> behav (not split between groups) is more appropriate.
>>>
>>>
>>>
>>>> And I assume demeaning should be done based on the whole-sample mean?
>>>> Not the mean per group? I keep on getting confused on that..
>>>>
>>>
>>> Yes.
>>>
>>>
>>>>
>>>> Last question: I see you did not add the intercept in the design (e.g.
>>>> EV1 all 1's); so, when exactly do you have to add it (and how does it or
>>>> does it not influence the need for demeaning)? I did add it in my design
>>>> before; is that a flaw? Did this likely influence my results? Is there
>>>> documentation on the role of the intercept here, and when to use it, and
>>>> when not, so I can also decide in future studies how to set up my design
>>>> matrix?
>>>>
>>>
>>> The intercept codes the overall mean. Here you want to compare the means
>>> of each group, so the intercept is split across both groups, which are
>>> tested with C5 and C6.
>>> The intercept can be modelled as a single EV, but this entails modifying
>>> other EVs.
>>>
>>> All the best,
>>>
>>> Anderson
>>>
>>>
>>>
>>>>
>>>> On Sat, Feb 21, 2015 at 10:45 AM, Anderson M. Winkler <
>>>> [log in to unmask]> wrote:
>>>>
>>>>> Hi Zsuzsi,
>>>>>
>>>>> For these hypotheses, you need in the design matrix:
>>>>>
>>>>> EV1: group 1
>>>>> EV2: group 2
>>>>> EV3: group 1 * behav 1
>>>>> EV4: group 2 * behav 1
>>>>> EV5: group 1 * behav 2
>>>>> EV6: group 2 * behav 2
>>>>> EV7: age
>>>>>
>>>>> Mean-center behav 1 and behav 2 before computing the interaction EVs.
>>>>>
>>>>> The contrasts will be:
>>>>> C1: [0 0 1 -1 0 0 0]: interaction group vs behav 1
>>>>> C2: [0 0 -1 1 0 0 0]: interaction group vs behav 1 (opposite sign)
>>>>> C3: [0 0 0 0 1 -1 0]: interaction group vs behav 2
>>>>> C4: [0 0 0 0 -1 1 0]: interaction group vs behav 2 (opposite sign)
>>>>> C5: [1 -1 0 0 0 0 0]: interaction group vs behav 1 (group 1 > group 2)
>>>>> C6: [-1 1 0 0 0 0 0]: interaction group vs behav 1 (group 1 < group 2)
>>>>>
>>>>> All these hypotheses can be done with t-tests, so no need for F-tests.
>>>>> The 6 contrasts mean multiple testing. You can use Bonferroni over the 6
>>>>> tests, so the significance level becomes 0.05/6 = 0.00833. It's not as
>>>>> conservative as it may appear, because the positive and negative versions
>>>>> can be treated as independent with data as you have.
>>>>>
>>>>> Still, if you want, you can run an F-test comprising C1, C3 and C5.
>>>>> The result of this test is just a shield to minimise multiple testing, as
>>>>> the interpretability is a bit difficult given that the respective t-tests
>>>>> test entirely different things.
>>>>>
>>>>> The above ignores the fact that behav 1 and behav 2 are correlated.
>>>>> Maybe you can consider using PCA to derive just 1 new behavioural score
>>>>> that captures the variance of these two.
>>>>>
>>>>> All the best,
>>>>>
>>>>> Anderson
>>>>>
>>>>>
>>>>> On 20 February 2015 at 12:11, Zsuzsika Sjoerds <[log in to unmask]>
>>>>> wrote:
>>>>>
>>>>>> Dear Anderson & FSL list,
>>>>>>
>>>>>> Following recent posts on the design matrix for e.g. randomise, I
>>>>>> started doubting about my own glm approach. I am originally trained with
>>>>>> the SPM gui, where one can build contrasts on the go (e.g. define post-hoc
>>>>>> t-contrasts only after seeing an interaction effect in F-contrast). For the
>>>>>> FSL/glm approach, all contrasts need to be built beforehand (also post-hoc
>>>>>> t-contrasts) to run at once in randomise, so an extra amount of
>>>>>> forward-planning is needed.
>>>>>> I read about several approaches that I had not applied so far. I hope
>>>>>> someone could help figure out what the best GLM design would be for my
>>>>>> analyses.
>>>>>>
>>>>>> I have a sample of 50 participants, split in two even groups (group1,
>>>>>> group2). First I want to compare the groups on FA values, as obtained by
>>>>>> TBSS, and second I want to regress the FA values (whole sample & group
>>>>>> interaction) with two behavioral measurements (behav1, behav2). These two
>>>>>> behavioral measurements negatively correlate with each other, and both also
>>>>>> correlate with age (one shows a positive, the other shows a negative
>>>>>> correlation). Therefore I want to add age as a nuisance variable, to make
>>>>>> sure I don't look at age effects.
>>>>>>
>>>>>> For my study I have the following 5 research questions:
>>>>>> - group difference on FA, irrespective of behavioral measurements
>>>>>> - (whole sample) association with behav1
>>>>>> - group * behav1 interaction (and post-hoc t-test, if the interaction
>>>>>> is significant)
>>>>>> - (whole sample) association with behav2
>>>>>> - group * behav2 interaction (and post-hoc t-test, if the interaction
>>>>>> is significant)
>>>>>>
>>>>>> eventually I want to apply this same approach on tractography data,
>>>>>> so it would be good to have things straight on a correct design at this
>>>>>> point.
>>>>>>
>>>>>> Until now I have created 5 separate design.mat files for the 5
>>>>>> questions above (mainly due to the order in which I explored my dataset and
>>>>>> tried out designs in the beginning), but I can imagine this is not optimal
>>>>>> due to multiple comparisons, and degrees of freedom?
>>>>>>
>>>>>>
>>>>>> Therefore my first question: can I create an optimal glm design that
>>>>>> combines (several of) these questions?
>>>>>>
>>>>>> For instance 3 designs:
>>>>>> 1. main group comparison, irrespective of behavioral measurement
>>>>>> 2. behav1, with EVs and contrasts exploring both a main effect of
>>>>>> behav1, and a group interaction
>>>>>> 3. behav2, with EVs and contrasts exploring both a main effect of
>>>>>> behav1, and a group interaction
>>>>>>
>>>>>> However, in a recent post I read that the main group difference
>>>>>> (irrespective of behavioral measurement) and the behavioral regression
>>>>>> could be entered in one design, together with the nuisance variable
>>>>>> (although now behavior * group interaction was studied in this post, as far
>>>>>> as I could see)
>>>>>> (
>>>>>> https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind1502&L=FSL&F=&S=&X=0911D30C4E89A0952E&Y=sjoerds.zs%40gmail.com&P=234382
>>>>>> ).
>>>>>> So then for me this would look like:
>>>>>> (EV1 = intercept? 50 ones??)
>>>>>> EV2 = group1 (25 ones, 25 zeros)
>>>>>> EV3 = group2 (25 zeros, 25 ones)
>>>>>> EV4 = nuisance (50 demeaned values based on whole sample mean)
>>>>>> EV5 = group1 behav (25 demeaned values - based on whole sample mean?
>>>>>> 25 zeros for group2)
>>>>>> EV6 = group2 behav (25 demeaned values - based on whole sample mean?
>>>>>> 25 zeros for group1)
>>>>>>
>>>>>> In that case it was suggested to simply make the F-contrast 0 1 -1 0
>>>>>> 0 0 for group differences, and two t-contrasts for possible post-hoc tests:
>>>>>> 0 1 -1 0 0 0
>>>>>> 0 -1 1 0 0 0
>>>>>>
>>>>>> And then I assume, the whole-sample regression with behav would be
>>>>>> contrasted as:
>>>>>>
>>>>>> t-contrast 0 0 0 0 1 1 (for a positive association)
>>>>>> and
>>>>>> t-contrast 0 0 0 0 -1 -1 (for a positive association)
>>>>>>
>>>>>> and group-interaction on behav would be:
>>>>>> f-contrast 0 0 0 0 1 -1 (plus respective t-contrasts if f-contrast
>>>>>> shows significance)
>>>>>>
>>>>>> However, not taking the two behav EVs (of interest) into account in
>>>>>> the first contrast (0 1 -1 0 0 0), I assume that these behav EVs are also
>>>>>> considered nuisance variables, and therefore they influence the explained
>>>>>> variance between the groups? This is the reason why I earlier built
>>>>>> separate GLMs for the non-behavior related group differences, versus
>>>>>> regression with behavior. But do I understand correctly now that it is also
>>>>>> fine to combine these EVs in one design? How does randomise see the
>>>>>> difference between nuisance variables and variables of interest then?
>>>>>> I do assume that the regressions with the two different behavioral
>>>>>> measurements should however be defined in two separate designs, especially
>>>>>> because of their colinearity. But if I combine pure (non-behavior related)
>>>>>> group difference EVs with behavioral EVs, I have the same first contrast (0
>>>>>> 1 -1 0 0 0) in both designs (for behav1 and behav2).. hence my confusion.
>>>>>>
>>>>>>
>>>>>> One other important question: I have manually demeaned both my behav
>>>>>> and nuisance (age) parameters. Therefore I don't add the -D in the command
>>>>>> in randomise, but did add an intercept as first EV (filed with ones, and
>>>>>> all following EVs move a number). But now I read that it was adviced
>>>>>> against it?
>>>>>> So, in my case: do I need to add an intercept, and how do I handle
>>>>>> demeaning??
>>>>>>
>>>>>> Thanks in advance!
>>>>>> Best,
>>>>>> Zsuzsi
>>>>>>
>>>>>> --
>>>>>> Z. Sjoerds, PhD
>>>>>> Postdoctoral researcher
>>>>>>
>>>>>> Max Planck Institute for Human Cognitive and Brain Sciences
>>>>>> Fellow-Group Cognitive and Affective Control of Behavioral Adaptation
>>>>>> Group Schlagenhauf, Room C211
>>>>>> Stephanstraβe 1A
>>>>>> 04103 Leipzig
>>>>>> Germany
>>>>>>
>>>>>> [T]: +49 (0) 341 9940 2471
>>>>>> [F]: +49 (0) 341 9940 2499
>>>>>> [E]: [log in to unmask] / [log in to unmask]
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>> --
>>>> *Z. Sjoerds, PhD*
>>>> *Postdoctoral researcher*
>>>>
>>>> *Max Planck Institute for Human Cognitive and Brain Sciences*
>>>> *Fellow-Group Cognitive and Affective Control of Behavioral Adaptation*
>>>> *Group Schlagenhauf, Room C211*
>>>> *Stephanstra*
>>>> *βe 1A**04103 Leipzig*
>>>> *Germany*
>>>>
>>>> *[T]: +49 (0) 341 9940 2471 <%2B49%20%280%29%20341%209940%202471>*
>>>> *[F]: +49 (0) 341 9940 2499 <%2B49%20%280%29%20341%209940%202499>*
>>>>
>>>> *[E]: [log in to unmask] / [log in to unmask] <[log in to unmask]>*
>>>>
>>>
>>>
>>
>>
>> --
>> *Z. Sjoerds, PhD*
>> *Postdoctoral researcher*
>>
>> *Max Planck Institute for Human Cognitive and Brain Sciences*
>> *Fellow-Group Cognitive and Affective Control of Behavioral Adaptation*
>> *Group Schlagenhauf, Room C211*
>> *Stephanstra*
>> *βe 1A**04103 Leipzig*
>> *Germany*
>>
>> *[T]: +49 (0) 341 9940 2471 <%2B49%20%280%29%20341%209940%202471>*
>> *[F]: +49 (0) 341 9940 2499 <%2B49%20%280%29%20341%209940%202499>*
>>
>> *[E]: [log in to unmask] / [log in to unmask] <[log in to unmask]>*
>>
>
>
--
*Z. Sjoerds, PhD*
*Postdoctoral researcher*
*Max Planck Institute for Human Cognitive and Brain Sciences*
*Fellow-Group Cognitive and Affective Control of Behavioral Adaptation*
*Group Schlagenhauf, Room C211*
*Stephanstra*
*βe 1A**04103 Leipzig*
*Germany*
*[T]: +49 (0) 341 9940 2471*
*[F]: +49 (0) 341 9940 2499*
*[E]: [log in to unmask] / [log in to unmask] <[log in to unmask]>*
